Wireless communication experiences non-stationary, fast fading channel conditions, especially for transceivers that move rapidly. This makes it difficult to estimate the channel to enable coherent signal detections.
Many channel estimation methods for frequency and time selective fading are known. One of the most widely used methods performs a recursive least-square (RLS) process, which has a good tracking ability and low complexity. The tracking performance can be improved with an order extended RLS process.
While the order extended RLS process offers good estimation accuracy in a high signal-to-noise ratio (SNR) regimes and for very fast fading channels, the estimation capability is severely degraded when the channel is slow fading, sparse in frequency, and very noisy.
For sparse channel estimations, a sparse RLS process, which uses compressive sensing based on L1-norm regularization, is known. Compressive sensing determines sparse solutions for under-determined linear systems. However, that method assumes a single-antenna at the transceiver. In addition, the performance degrades in a very rapid fading channel because that method is based on conventional zero-order channel estimation.